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Neural Networks and DeepLearning

Syllabus

  • Definitions: Data Science, Artificial Intelligence, Machine Learning, Neural Networks and Deep Learning
  • Supervised Machine Learning: Concepts and Process Workflow
  • Applied Math and Machine Learning Basics
  • Introduction: Logistic Regression and Neural Networks
  • Development Frameworks: PyTorch / Tensorflow
  • Types of Neural networks
    • Perceptron (P), FeedForward (FF), Deep Feed Forward (DFF), Extreme Learning Machines (ELM)
    • Recurrent Neural Network (RNN), Long / Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Deep Residual Networks (DRN), Transformer Neural Networks (Transformers)
    • Auto Encoders (AE, VAE, DAE, SAE)
    • Convolution Neural Networks (CNN), Deconvolution Neural Networks (DNN), DCIGN
    • Generative Adversarial Network (GAN)
    • Other Types (LSM, KN, NTM, SVM)
  • NN Topics: Representation Learning, Reinforcement Learning, Transfer Learning

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